近期，我院教师肖蓓蓓（通讯），研究生杨磊等的研究成果“Unveiling ‘Sabatier principle’ for electrocatalytic nitric oxide reduction on single cluster catalysts: A DFT and machine learning guideline”在《Chemical Engineering Journal》（IF=15.1）上发表。
论文简介如下：
As a revolutionary chemical engineering, the electrocatalytic synthesis of ammonia (NH3) has become one alternative option for green hydrogen storage. However, under the guidance of Sabatier principle, favored single atom catalyst (SAC) hardly gets rid of the perplexity of element selection. Here, a span-new structure-activity correlation for nitric oxide reduction reaction (NORR) was well-proposed on single cluster catalyst (SCC), which not only contributes to improve the activity with the cluster growth, but also breaks the consistent pattern of catalytic performance toward designed SAC. By performing DFT calculations, Co4@GaS was selected out because of its unexceptionable activity, only − 0.06 V for working potential rarely reported yet. More eye-catching, as the displacement of activity descriptors obtained from elusory electronic characteristics only, a universal expression that handles free energy change was put forward with the assistance of machine learning (ML). This work may open a fresh avenue for the rational and straightforward design of desirable catalysts and even the development of interdisciplinary in chemistry.
全文下载：
https://www.sciencedirect.com/science/article/pii/S1385894723025548
